In this paper, we consider the order statistics from a newly-introduced lifetime distribution called the XLindley distribution. We have derived explicit closed form expressions for the single moments and product moments of order statistics from the XLindley distribution. Utilizing these expressions, we calculated the means, variances, and covariances of order statistics for sample sizes ranging from n = 1 to n = 10 and arbitrarily selected parameter values. Additionally, these moments allow us to identify the best linear unbiased estimators and best linear invariant estimators for the location and scale parameters based on both complete samples and Type-II right censored samples. We also address the linear prediction of unobserved order statistics based on Type-II right-censored samples. We also explore the formulation of confidence intervals for location and scale parameters, along with prediction intervals for unobserved order statistics. To provide comparison and illustration, we conduct a simulation study and analyze a real data example. Finally, we conclude with several remarks.

Mazucheli et al. (2019) introduced the unit-Gompertz (UG) distribution and studied some of its properties. More specifically, they considered the random variable X =exp(-Y), where Y has the Gompertz distribution. In this paper, we consider the lower k-record values from this distribution. We obtain exact explicit expressions as well as several recurrence relations for the single and product moments of lower k-record values and then we use these results to compute the means, variances and the covariances of the lower k-record values. We make use of these calculated moments to find the best linear unbiased estimators (BLUEs) of the location and scale parameters of the UG distribution. Applying the relation between the BLUE and the best linear invariant estimator (BLIE), we obtain the BLIEs of the location and scale parameters, as well. In addition, based on the observed k-records, we investigate how to obtain the best linear unbiased predictor (BLUP) and best linear invariant predictor (BLIP) for a future k-record value. Confidence intervals for the unknown parameters and prediction intervals for future k-records are also discussed. A simulation study is performed to assess the point and interval estimators and predictors proposed in the paper. The results show that the BLIE and BLIP outperform the BLUE and BLIP, in the sense of mean squared error criterion, respectively. Finally, a real data set pertaining to COVID-19 2-records is analyzed.

This study develops explicit algebraic expressions for the single and product moments of order statistics derived from the generalized Bilal (GB) distribution. These expressions facilitate the computation of means, variances and covariances of order statistics for sample sizes up to n = 10 {n=10} with specified parameter values. The derived moments serve as the foundation for constructing the best linear unbiased estimators (BLUEs) and best linear invariant estimators (BLIEs) for the location and scale parameters applicable to both complete and type-II right censored samples. Additionally, the study explores the prediction of unobserved order statistics in type-II right censored samples. The theoretical results are validated through a simulation study, while a real data example highlights their practical utility. These findings establish a robust framework for statistical inference based on order statistics from the GB distribution.

This paper presents a new type of Sushila distribution that provides greater flexibility for modelling lifetime data. This model, called the Sushila-Poisson (SP) distribution, is created by combining the Sushila and Poisson distributions. The three-parameter SP distribution represents various shapes of hazard rate functions, including upside-down bathtub, bathtub-shaped, increasing, and decreasing hazard rates, which are commonly encountered in fields such as medicine, engineering, economics, and the natural sciences. Therefore, the proposed model offers great potential for applications in these areas. The new model includes several known distributions, such as the Lindley, Lindley-Poisson, and Sushila distributions, as special cases. Several statistical properties of the SP distribution have been derived in this study. Simulation studies were conducted to examine the performance of the maximum likelihood estimators, which were developed using the Expectation-Maximization (EM) algorithm. The flexibility of the new model was further demonstrated through its application to three real data sets.

In this article, we consider the estimation of the stress-strength reliability parameter for the inverse Lindley distribution based on lower record values. The maximum likelihood estimator and its asymptotic distribution are obtained. An approximate classical confidence interval, as well as two bootstrap-type confidence intervals for the reliability parameter are derived. The Bayesian inference for the parameter has been considered using Tierney and Kadane’s approximation method, as well as two Monte Carlo methods, namely the Metropolis-Hastings and importance sampling techniques under both symmetric and asymmetric loss functions. Besides, the Chen and Shao shortest width credible intervals are constructed for the stress-strength parameter. A simulation study and a real data example are conducted to explore and compare the performances of the presented results.

Although the investigation of the stress-strength parameter has a long history due to its importance, the investigation of this parameter in distributions with a limited range, such as (0, 1), has received less attention. Thus, in this article, we study the stress-strength parameter for Unit Generalized Gompertz (UGG) distribution, which can cover a variety of data, including data skewed ones. Considering the flexibility of the shape of the density function and its hazard rate function, we expect that this distribution would possess more applicability and can be fitted to different data sets. With this aim, we discuss the parameter R for the UGG distribution and obtain different methods of parameter estimation such as maximum likelihood, bootstrap, and Bayesian. The performance of the estimators has been done by simulation, and finally, the presented content has been applied to a real data set.

مقدمه: این پژوهش با هدف مقایسه اثربخشی آموزش کاهش استرس مبتنی بر ذهن آگاهی (MBSR) و آموزش متمرکز بر شفقت (CFT) بر سبک های مقابله با استرس در بیماران مبتلا به بیماری عروق کرونر انجام شد.روش کار: جامعه آماری این مطالعه بالینی را کلیه بیماران مبتلا به بیماری عروق کرونر قلب مراجعه کننده به کلینیک قلب و عروق سامان مشهد در تیر تا مرداد 1400 تشکیل می دادند که 45 بیمار به روش داوطلبانه انتخاب شدند. آنها به طور تصادفی در دو گروه آزمایش و یک گروه کنترل قرار گرفتند. گروه‌های آزمایشی MBSR را بر اساس طرح آموزشی Kabat-Zinn (2005) یا CFT بر اساس طرح آموزشی گیلبرت (2009) در 8 جلسه دریافت کردند. شرکت کنندگان پرسشنامه سبک های مقابله با استرس لازاروس و فولکمن (1988) را تکمیل کردند. داده ها با استفاده از آزمون تحلیل واریانس مختلط با اندازه گیری های مکرر و نرم افزار SPSS-23 مورد تجزیه و تحلیل قرار گرفت.یافته ها: نتایج نشان داد که آموزش مبتنی بر شفقت باعث افزایش سبک مقابله مستقیم و خویشتن داری و کاهش سبک مقابله ای اجتنابی و سبک مقابله ای گریز-اجتنابی می شود. MBSR سبک مقابله با مسئولیت، حل مسئله برنامه ریزی شده، ارزیابی مجدد مثبت و سبک مقابله فرار-اجتناب را به طور قابل توجهی کاهش می دهد (P<01/0). همچنین اثربخشی MBSR بر مسئولیت پذیری به طور معنی داری بیشتر از CFT بود (05/0 P<).نتیجه‌گیری: به نظر می‌رسد که آموزش کاهش استرس مبتنی بر ذهن‌آگاهی و آموزش متمرکز بر شفقت می‌تواند وضعیت روانی بیماران مبتلا به بیماری عروق کرونر را بهبود بخشد.

Explicit algebraic expressions for both single and product moments of order statistics from the exponentiated moment exponential (EME) distribution are derived. By using these expressions, we have computed the means, variances and covariances of order statistics for n = 1(1)10 and for arbitrarily chosen parameter values. Further, these moments are used to determine the best linear unbiased estimators (BLUEs) and best linear invariant estimators (BLIEs) of the location and scale parameters based on complete as well as Type-II right censored samples. Prediction of unobserved order statistics in Type-II right censored samples is also discussed. A simulation study and a real data example are presented for the sake of comparison and illustration. The concluding remarks are given at the end.

The aim of this study was to design future studies model based on strategic thinking approach in Farhangian University. This research is an applied research in terms of purpose and a qualitative research in terms of method, done by Delphi technique. The statistical population was consisted of experts, professionals and faculty members of North, South and Razavi provinces of Farhangian University. 25 of who were selected by purpose-based. They have responded to questionnaires in four stages, confirmed face, and content validity. Cronbach's alpha coefficient as reliability estimate of final questionnaire was calculated 0.91. Kendal coefficient of concordance in four stages has been 0.728. Considering that the agreement, it can be said that the experts have reached a high consensus on the dimensions of the components. Results of Delphi's technique showed 6 dimensions in future research (foresight, environmental awareness, creativity and innovation, limiters, regulators, leadership) and 31 components and 5 dimensions in strategic thinking (individual, group, organizational, Intuitive and systematic) and 17 components.Findings showed that the research model has an acceptable level and is consistent with the research background.

This paper develops a new method to estimate the parameters in mixture models. Traditionally, the parameter estimation in mixture models is performed from a likelihood point of view by exploiting the expectation maximization (EM) method. In this paper, however, we utilize the Least Square Principle. Based on this principle, we propose an iterative algorithm called Iterative Weighted least Square (IWLS) to estimate the parameters. Through comparative study, we demonstrate the superiority of our method compared to EM method. We show that IWLS method outperforms EM in both accuracy and the number of iterations required for convergence.

Mazucheli et al. (Statistica 79:25–43, 2019) introduced a new transformed model called the unit-Gompertz (UG) distribution which exhibits right-skewed (uni-modal) and reversed-J shaped density and its hazard rate function can be increasing and increasing-decreasing-increasing. They worked on the estimation of the model parameters based on complete data sets. In this paper, by using lower record values and inter-record times, we develop inference procedures for the estimation of the parameters and prediction of future record values for the UG distribution. First, we derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariances between two lower record values. Next, we obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Further, we obtain the Bayes estimators under the assumption that the model parameters follow a joint bivariate density function. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential) loss functions with the help of the Tierney–Kadane’s method and Metropolis–Hastings algorithm. Finally, we compute Bayesian point predictors for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.

In this paper we are concerned with variable selection in finite mixture of semiparametric regression models. This task consists of model selection for non parametric component and variable selection for parametric part. Thus, we encountered separate model selections for every non parametric component of each sub model. To overcome this computational burden, we introduced a class of variable selection procedures for finite mixture of semiparametric regression models using penalized approach for variable selection. It is shown that the new method is consistent for variable selection. Simulations show that the performance of proposed method is good, and it consequently improves pervious works in this area and also requires much less computing power than existing methods.

Selection of the important variables is one of the most important model selection problems in statistical applications. In this article, we address variable selection in finite mixture of generalized semiparametric models. To overcome computational burden, we introduce a class of variable selection procedures for finite mixture of generalized semiparametric models using penalized approach for variable selection. Estimation of nonparametric component will be done via multivariate kernel regression. It is shown that the new method is consistent for variable selection and the performance of proposed method will be assessed via simulation.

We decided to determine the percentage of hypertensive patients whose blood pressure (BP) measurements were within recommended controlled range and to identify predictive factors for controlled BP. In this study carried out in 2014, 280 patients were included consecutively through sampling from both university and private medical centers/pharmacies in four Iranian cities. Demographic data as well as information about duration of HTN and prescribed medications, admission to emergency department (ED) because of HTN crisis, comorbidities, and control of HTN during the last 6 months by a healthcare provider were gathered. Adherence to anti-hypertensives was also determined using the validated Persian version of the 8-item Morisky Medication Adherence Scale (MMAS-8). Controlled BP was defined as systolic BP< 140 and diastolic BP< 90 mmHg in non-diabetics and < 130/80 mmHg in diabetics. Of 280 patients, 122 subjects (43.6%) had controlled BP. Among 55 diabetics, only two patients (3.6%) had controlled BP. Multiple logistic regression revealed the following variables as significant predictors of controlled BP: higher MMAS-8 score (adjusted odds ratio (OR)= 1.19, P= 0.03), fewer number of comorbid conditions (adjusted OR= 0.71, P = 0.03), having occupation as clerk/military personnel (adjusted OR= 1.03, P= 0.04), and not having history of ED admission during the last 6 months because of HTN crisis (adjusted OR= 2.11, P= 0.01). Considerable number of the studied patients had uncontrolled BP. Regarding the dramatic consequences of uncontrolled high BP in long term, it is advisable that careful attention by health care providers to the aforementioned factors could raise the likelihood of achieving controlled BP.

In the meta-analysis of clinical trials, usually the data of each trail summarized by one or more outcome measure estimates which reported along with their standard errors. In the case that summary data are multi-dimensional, usually, the data analysis will be performed in the form of a number of separated univariate analysis. In such a case the correlation between summary statistics would be ignored. In contrast, a multivariate meta-analysis model, use from these correlations synthesizes the outcomes, jointly to estimate the multiple pooled effects simultaneously. In this paper, we present a nonparametric Bayesian bivariate random effect meta-analysis.